AVO inversion in V (x, z) media

Stanford Exploration Project, Report 97, July 8, 1998, pages 275–294
AVO inversion in V (x, z) media
Yalei Sun and Wenjie Dong1
keywords: 2.5-D Kirchhoff integral, AVO inversion, fluid-line section
ABSTRACT
We implement a new Kirchhoff-typed AVO inversion scheme in V (x, z) media.
The WKBJ Green’s function is calculated using a finite-difference scheme. We
propose a pair of Kirchhoff inversion operators which have more obvious physical
meaning. By analyzing the Kirchhoff inversion operator, we find out an unique
relationship between the weighting function and the kinematic equation, which
is very important to recover the true amplitude of the reflection coefficient. Our
scheme is a two-step AVO inversion approach. Common-image gathers (CIG)
are generated in the first step. These common-image gathers can be used to
update the velocity model and reduce the influence of velocity error in the final
AVO inversion results. AVO intercepts and slopes are estimated in the second
step using a least-squares procedure. Finally, a fluid-line section is generated to
highlight the existence of Vp /Vs anomaly. One dipping-layered synthetic example
demonstrates the accuracy of our scheme and the influence of NMO stretch on
the estimated AVO coefficients. The result from a field data example, the Mobil
AVO dataset, shows a strong Vp /Vs anomaly in the middle of the section that
may be a potential hydrocarbon indicator.
INTRODUCTION
Conventional AVO analysis extracts the intercept and slope from NMO-corrected
CMP gathers. Since no imaging capability is incorporated, diffraction energy is not
properly analyzed. Diffraction-corrupted intercept and slope sections may lead to
false hydrocarbon indications. The influence of migration/inversion on AVO analysis
has been addressed by several authors (Lumley et al., 1995; Mosher et al., 1996; Dong
and Keys, 1997). Lumley et al. (1995) use the conventional common-offset prestack
time migration to collapse the diffraction energy. Mosher et al. (1996) also use the
prestack time migration technique. In order to improve the lateral resolution and
spatial positioning of AVO anomalies, they choose common-angle sections instead
of common-offset sections. The most important problem with the time migration
schemes is that they cannot handle large lateral velocity and structure variations
1
email: yalei@sep.stanford.edu,wenjie dong@email.mobil.com
275
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easily. The time imaging error will consequently produce mispositioning of AVO
anomalies in the spatial domain. Dong and Keys (1997) propose a prestack depth
inversion scheme. They assume that the earth satisfies a locally 1-D layered velocity model to make the algorithm efficient. The inversion is of Kirchhoff-typed and
implemented in the common-midpoint gather. This locally 1-D assumption restricts
their schemes to handle only moderate lateral velocity and structure variations. In
this paper, we propose an AVO inversion scheme for 2-D media. The WKBJ Green’s
function is calculated by a finite-difference algorithm. A 2.5-D Kirchhoff integral is
used in the inversion. Common-image gathers (CIG) are produced as a by-product,
that can be used to quality-control the accuracy of velocity model. We first derive
a new form of the 2.5-D Kirchhoff integral formula in V (x, z) media and relate it to
the WKBJ Green’s function. Then we discuss the characteristics of the weighting
function in the Kirchhoff integral. We show the effect of the integral aperture on the
estimated amplitude of the reflection coefficient. Finally, we discuss the results of
applying the new algorithm to synthetic and field data.
THEORY OF 2.5-D KIRCHHOFF INVERSION IN V (X, Z) MEDIA
In the spatial and frequency domain, the 3-D acoustic wave equation can be formulated as
#
"
ω2
2
G(x, xs , ω) = −δ(x − xs )
(1)
∇ + 2
c (x)
where G(x, xs, ω) can be approximated by the WKBJ Green’s function
G(x, xs , ω) ∼ A(x, xs )eiωτ (x,xs )
(2)
where τ (x, xs ) is the traveltime from source xs to an arbitrary point x. Using the
WKBJ Green’s function, Beylkin (1985) gave an inversion formula in 3-D media
c2 (x)
α(x) ∼
8π 3
Z Z
S0
d2 ξ
Z
|h(x, ξ)|
dωF (ω)e−iω[τ (x,xs )+τ (x,xr )] D(ω, ξ).
A(x, xs )A(x, xr )
(3)
In the above formula, α(x) is the perturbation to the background velocity c(x). The
updated velocity model is given by
v −2 (x) = c−2 (x) [1 + α(x)] .
(4)
S0 is the 2-D integral surface. h(x, ξ) is introduced by Beylkin (1985), which is associated with the ray curvature. A(x, xs ) and A(x, xr ) are the WKBJ Green’s function.
F (ω) is a high-pass filter determined the source. D(ω, ξ) represents the observed
data at xr due to the source xs . Bleistein et al.(1987) specialize the 3-D formula to
the 2.5-D geometry using the method of stationary phase. The corresponding 2.5-D
inversion formula is
α(x) ∼ 2
s
h
i
2Z
dξ 1 + c2 (x)ps · pr
π
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s
277
1
1
Ar
As
+
n̂s · ps0 σs0 + n̂r · pr0 σr0
σs0 σr0
Ar
As
Z
dω
√ F (ω)e−iω(τs +τr ) D(ω, ξ)
iω
(5)
Here, ps and pr are the slowness vectors at the imaging location pointing to the source
and receiver respectively. σs0 and σr0 are the parameters defined by the following
equations
σs0 =
Z
τs
0
c(x)dτ,
σr0 =
Z
τr
0
c(x)dτ.
(6)
n̂s and n̂r are unit downward normals at the source and receiver points respectively.
ps0 and pr0 are the slowness vectors at the source and receiver points respectively.
This inversion formula is only valid in the high-frequency limit. Under such circumstances, it is better to process data for the upward normal derivative ∂α/∂n at each
discontinuity surface of α(x). ∂α/∂n is a sum of weighted singular functions with
peaks on the reflectors. Therefore, ∂α/∂n actually provides an image of the subsurface. Using the Fourier transform, we can obtain the following 2.5-D formula for
∂α/∂n.
Z
h
i3
4
∂α
dξ 1 + c2 (x)ps · pr 2
(x) ∼ √
∂n
πc(x)
s
i
1
1 h
−1
n̂s · ps0 A2s (x, xs )σs0 + n̂r · pr0 A2r (x, xr )σr0
[As Ar ]
+
σ s0 σ r0
Z
√
dω iωF (ω)e−iω(τs +τr ) D(ω, ξ)
(7)
Bleistein et al.(1987) also shows that ∂α/∂n can be related to the reflection coefficient
on the interface by
∂α
∼ 4 cos2 θR(x, θ)γ(x)
(8)
∂n
γ(x) in the singular function of the model space. R(x, θ) is determined by the changes
of velocity and density above and below the interface and the incident angle on the
interface
R(x, θ) =
q
cdown (x)ρdown cos θ − ρup c2up(x) − c2down (x) sin2 θ
q
cdown (x)ρdown cos θ + ρup c2up (x) − c2down (x) sin2 θ
.
(9)
In order to determine R(x, θ) from ∂α/∂n, we have to determine cos θ. In their paper,
Bleistein et al.(1987) proposed another inversion operator β(x, z) with a kernel slightly
modified from that in ∂α/∂n.
Z
h
i1
2
β(x) ∼ √
dξ 1 + c2 (x)ps · pr 2
πc(x)
s
i
1 h
1
−1
n̂s · ps0 A2s (x, xs )σs0 + n̂r · pr0 A2r (x, xr )σr0
+
[As Ar ]
σ s0 σ r0
Z
√
(10)
dω iωF (ω)e−iω(τs +τr ) D(ω, ξ)
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There is a simple relation between ∂α/∂n, β(x, z), and cos θ, that is
2
cos θ =
∂α
(peak)
∂n
4β(peak)
.
(11)
With cos θ known, we can use ∂α/∂n and cos θ to calculate the reflection coefficient
R(x, θ). From R(x, θ) and cos θ, we can further estimate the AVO coefficients: intercept and slope. Instead of using ∂α/∂n and β(x, z), we propose another pair of
inversion operators that can determine cos θ in a similar, but more straightforward
and physically meaningful manner. The first operator gives the reflection coefficient
at the specular incident angle
1
R(x, θ) ∼ √
πc(x)
[As Ar ]
Z
Z
−1
dξ
s
i
1
1 h
n̂s · ps0 A2s (x, xs )σs0 + n̂r · pr0 A2r (x, xr )σr0
+
σs0 σr0
√
dω iωF (ω)e−iω(τs +τr ) D(ω, ξ)
(12)
The second gives the reflection coefficient multiplied by cos θ
1
2πc(x)
R0 (x, θ) ∼ √
[As Ar ]−1
Z
Z
s
h
dξ 1 + c2 (x)ps · pr
i1
2
i
1
1 h
n̂s · ps0 A2s (x, xs )σs0 + n̂r · pr0 A2r (x, xr )σr0
+
σ s0 σ r0
√
dω iωF (ω)e−iω(τs +τr ) D(ω, ξ)
(13)
From R(x, θ) and R0 (x, θ), we can easily calculate cos θ
cos θ =
R0 (x, θ)
.
R(x, θ)
(14)
In order to reduce the sensitivity of cos θ to noise in the data, we use a least-squares
procedures to estimate cos θ. First, we define a small window (n x × nz ). Within the
window, we can get a series of equations
= R0 (x1 , z1 , θ)
= R0 (x2 , z2 , θ)
·
·
·
R(xn , zn , θ) cos θ = R0 (xn , zn , θ)
R(x1 , z1 , θ) cos θ
R(x2 , z2 , θ) cos θ
(15)
the least-squares sense estimate of cos θ is then
cos θ =
Pn
xi ,zi
R(x1 , z1 , θ)R0 (x1 , z1 , θ)
.
2
xi ,zi R (x1 , z1 , θ)
Pn
(16)
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AVO THEORY IN ACOUSTIC AND ELASTIC MEDIA
Under the assumption of small incident angle, there is a well-known linearized Zoeppritz equation (Aki and Richards, 1980). Because we only consider the incident angle
less than 35 degree, we have omitted the C term in the original form. For the acoustic
and elastic media, the expressions for the reflection coefficients are different.
Acoustic AVO approximation
R ≈ A + B tan2 θ
(17)
(18)
where
A ≈
B ≈
1 δV
2
V
1 δV
2 V
+
δρ
ρ
Elastic AVO approximation
R ≈ A + B sin2 θ
where
A ≈
B ≈
1 δVp
+ δρ
2
Vp
ρ 2
1 δVp
− 2 VVps
2 Vp
(19)
s
2 δV
+
Vs
δρ
ρ
(20)
Using the reflection coefficient R and specular incident angle θ, we find the solution
for intercept and slope is a least-squares problem.
R1 = A + Bf (θ1 )
R2 = A + Bf (θ2 )
·
·
·
Rn = A + Bf (θn )
(21)
The resulting estimates of A and B are given by
"
A
B
#
=
"
P
N
N
f (θ )
PN
PNi 2 i
i f (θi )
i f (θi )
#"
PN
Ri
PN
i Ri f (θi )
i
#
(22)
Getting AVO intercept and slope is not our final goal. The purpose of AVO analysis
is to display the Vp /Vs anomaly in the subsurface. This anomaly is a very important
hydrocarbon indication, especially for gas-charged reservoirs. Here we use the fluidline technique to highlight this anomaly. Assume there is a linear relation
AX + B = 0
(23)
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between intercept A and slope B. We specify a window with reasonable size and
use least squares algorithm to estimate the coefficient X. Similar to cos θ, we get an
expression for X
Pn
A(xi , zi )B(xi , zi )
X = xi ,zPi n
.
(24)
2
xi ,zi A (xi , zi )
The AX+B section is called the fluid-line section, which highlights the V p /Vs anomaly.
PARAMETER ANALYSIS
The 2.5-D Kirchhoff inversion can be viewed as a weighted Kirchhoff depth migration.
In other words, if there is no middle row in equation (12), the final result will be a
2-D Kirchhoff depth migration in V (x, z) media. In this section, we investigate the
relationship between the two key components in equation (12) in the homogeneous
medium.
Weighting function
The weighting function determines the contribution of each data sample to the image.
The weighting function depends on the locations of source, receiver, and diffractor.
w(xs, xr ; x) = [As Ar ]
−1
s
i
1 h
1
+
n̂s · ps0 A2s (x, xs )σs0 + n̂r · pr0 A2r (x, xr )σr0
σs0 σr0
(25)
Double-square-root (DSR) equation
The DSR equation is the kinematic relation between source, receiver, and diffractor
in the homogeneous media.
τ (xs , xr ; x) = τs + τr
q
1 q
2
2
2
2
=
(xs − x) + (zs − z) + (xr − x) + (zr − z)
V
(26)
It is worth investigating the relationship between these two components and other
parameters, such as image depth, integral aperture, velocity, and offset, etc. In order
to simplify the problem, we assume a homogeneous media and discuss the dependence
of weighting function and DSR equation on other parameters, such as offset, depth,
and velocity. The DSR equation is a function of imaging depth, velocity, and offset.
As shown in Figure 1, with increasing imaging depth, the hyperbolic curve becomes
flatter. Therefore, anti-aliasing requirements in the deep zone are not as severe as it
is in the shallow zone. Similarly, high velocity corresponds to a flattened hyperbola.
Large offset has a similar effect. Actually, if we view the offset response in 3-D, it is
the famous ”Cheops pyramid” (Claerbout, 1985). We then take the first and second
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derivatives of the hyperbolic curves. As show in Figure 2, with the increase of offset,
the first derivative has two inflection points. Correspondingly, two peak values show
up in the second derivative for non-zero offset. In a constant velocity medium, the
Velocity
Depth
Offset
12
10
10
10
8
6
Time (sec)
Time (sec)
Time (sec)
8
8
6
6
4
4
4
2
-1.0
-0.5
0
Midpoint (m)
0.5
1.0
x10 4
-1.0
-0.5
0
Midpoint (m)
0.5
1.0
x10 4
2
-1.0
-0.5
0
Midpoint (m)
0.5
1.0
x10 4
Figure 1: Cheops pyramid changes with different parameters. (L) From top to bottom, the hyperbolic moveout curves become flatter when velocity increases. (M) From
bottom to top, the curves become flatter with the increase of depth. (R) From bottom to top, the hyperbolic curves change from zero to nonzero offset. yalei1-cheops
[NR]
weighting function depends only on imaging depth and offset. As shown in Figure 3,
the weighting function has a double-peaked shape in non-zero offset. This feature is
very interesting. Intuitively, it is very natural to think that the data value located
right in the middle of the panel should have the largest contribution to the image. The
double-peaked weighing function in the case of common-offset configuration suggests
that the largest contribution to the image is not from the middle of the integral curve,
but from the two flanks. Therefore, it is very important to include the two peaks to
get a true-amplitude image when choosing the integral aperture.
SYNTHETIC DATASET
Figure 4 shows a simple 2-D acoustic model based on one used by Dong and Keys
(1997), as shown in Figure 4. The only difference is that all the layers here have a
10 degree dipping angle. For the first interface, there is no velocity change and only
density change. According to equation (9), the reflection coefficient is constant, 0.05.
Similarly, we can reach the same result from the acoustic AVO approximation. The
second layer has changes in velocity and density, but in opposite signs. Therefore,
these two changes cancel each other out and give a zero-valued intercept. Slope B
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First derivative
Cheops pyramid
Second derivative
x10 -3
x10 -7
1.0
6
10
5
0.5
8
Time (sec)
Time (sec)
Time (sec)
4
0
3
6
2
-0.5
4
1
-1.0
2
-1.0
-0.5
0
Midpoint (m)
0.5
1.0
x10 4
-1.0
-0.5
0
Midpoint (m)
0.5
1.0
x10 4
-1.0
-0.5
0
Midpoint (m)
0.5
1.0
x10 4
Figure 2: Cheops pyramid, first, and second derivatives. (L) Cheops pyramid changes
from zero to nonzero offset. (M) The first derivative has two inflections points near
the middle of the panel in the case of non-zero offset. (R) The second derivative has
a corresponding double-peaked shape in non-zero offset. yalei1-deri [NR]
Depth
Offset
160
140
140
120
Amplitude
Amplitude
120
100
100
80
80
60
60
-1.0
-0.5
0
Midpoint (m)
0.5
1.0
x10 4
-1.0
-0.5
0
Midpoint (m)
0.5
1.0
x10 4
Figure 3: The weighting function changes with depth and offset. (L) With increasing
depth, the double peaks smear out. (R) From zero to nonzero offset, the weighting
function goes from single-peaked to double-peaked shape. yalei1-weight [NR]
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is equal to 0.05. Reflection coefficient R increases from zero to nonzero value with
the increase of the incident angle. The third interface has only a velocity change and
no density change. The velocity drops across the interface and results in a negative
intercept and slope. We use an acoustic modeling program developed by Dong, which
Midpoint (m)
c=1737.4 (m/s)
ρ =1.9 (g/cm^3)
Depth (m)
10
A=0.05
B=0.0
2000
c=1737.4 (m/s)
ρ =2.1 (g/cm^3)
A=0.0 B=0.05
3000
A=−0.05
4100
0
c=1920.2 (m/s)
ρ =1.9 (g/cm^3)
B=−0.05
c=1737.4 (m/s)
ρ =1.9 (g/cm^3)
Figure 4: Dipping acoustic velocity model used in generating the synthetic dataset.
yalei1-model [NR]
is based on the reflectivity method (Müller, 1985). For such kind of layer model, the
modeling result is not only kinematically, but also dynamically exact. As shown in
the following result, such an accurate modeling program is very helpful for verifying
the performance of our inversion program. Figure 5 is a common-shot gather. The
first two events have a similar pattern, except that the second one goes to a zerovalued amplitude in the near offset. However, the third event shows an opposite
pattern. Figure 6 shows an image gather from the inversion result. Since the correct
velocity model has been used in calculating the WKBJ Green’s function, the three
events have been flattened in the image gather. However, due to the NMO stretching
effect, the events broaden from near to far offset. One way to check the accuracy of
our inversion result is to pick the peak amplitude along the three events and then
compare it with the theoretical solution. Figure 7 shows that the numerical results
match the theoretical ones very accurately. Figure 8 and 9 shows the intercept A and
slope B estimated from the inversion result. Compared with the theoretical results
under acoustic approximation, our solution matches the theoretical one very well.
These two figures also show the stretching effect very clearly. How to remove this
stretch effect efficiently is our next research topic.
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0
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Offset (m)
2000
3000
1000
4000
1
Time (sec)
2
3
4
Common-Shot Gather
Figure 5: Common-shot gather generated from the dipping velocity model using the
reflectivity method. yalei1-shot [NR]
0
1000
Offset (m)
2000
3000
4000
1000
1500
1500
2000
2000
Depth (m)
Depth (m)
1000
2500
1000
Offset (m)
2000
3000
4000
2500
3000
3000
3500
3500
4000
0
4000
Smooth Model (R)
Smooth Model (Rcos)
Figure 6: Common-image gather of the inversion result. (L) R as a function of offset.
(R) R cos θ as a function of offset. yalei1-dip-cig [NR]
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Coeff R (smooth)
Coeff R (theory)
0.08
0.08
0.06
0.06
Magnitude
0.10
Magnitude
0.10
0.04
0.04
0.02
0.02
0
0
1000
2000
3000
Offset (m)
0
4000
0
Coeff Rcos (smooth)
2000
3000
Offset (m)
4000
Coeff Rcos (theory)
0.10
0.08
0.08
0.06
0.06
Magnitude
0.10
Magnitude
1000
0.04
0.04
0.02
0.02
0
0
1000
2000
3000
Offset (m)
4000
0
0
1000
2000
3000
Offset (m)
4000
Figure 7: Comparison of numerical result and theoretical result. (TL) Numerical R. (TR) Theoretical R. (BL) Numerical R cos θ. (BR) Theoretical R cos θ.
yalei1-compare [NR]
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Figure 8: AVO coefficients A and B. (Top) Intercept A. (Bottom) Slope B. The
stretch effect is very obvious in the first wavelet of slope B. Since the transmission
effect has not been taken into account, the absolute values for the second and third
events are less accurate. yalei1-dip-avo [NR]
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Figure 9: Crossplot of intercept A and slope B. The solid curve represents the first
event, the dashed-line curve corresponds to the second one, and the dashed-dotted
curve is linked with the third event. The swirly nature of the curves is due to NMO
stretch (Dong, 1996). The extent of stretching effect can be evaluated by the distance
between the curves and the original point. yalei1-crossplot [NR]
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THE MOBIL AVO DATA AND ITS RESULT
The Mobil AVO dataset is a marine dataset collected from the North Sea. The dataset
contains strong water-bottom multiples. Before AVO analysis, some processing procedures have been applied to the data to remove the multiple energy. All together,
there are 952 CMP gathers with 25m sampling intervals. Each gather contains 60
traces, the offset sampling interval is 50m and the near offset is 288m. The trace
length is 1000 samples (sampling rate = 4ms). There are two well logs available at
CMP-809 and CMP-1571. In well CMP-809, the density, V p, and Vs were recorded
from 1km to 3.15km. On the basis of this well’s information, Dong and Keys (1997)
built up a 12-layer (some with vertical gradient) background velocity model for the
inversion. Since our new approach can output common-image gathers (CIG), we initially use this model in our inversion and then check the accuracy of this layered
model. As shown in 10, the events from the old velocity model bend upwards, which
means the interval velocity in the old model is lower than the correct one. We then
conducted a conventional velocity analysis. After converting the RMS velocity model
into an interval velocity model, we applied the new velocity model to the dataset and
produced the new common-image gather at the same CMP location. It is clear that
the new velocity model is better for imaging and inversion (Figure 10). By stacking
the common-offset inversion result, we got a R and R 0 section (Figure 11). After obtaining the Kirchhoff inversion result, we estimated the cosine of the specular angle θ.
According to the elastic AVO approximation theory, we estimated of intercept A and
slope B, as shown in Figure 12. Furthermore, we combined the intercept and slope
sections and produced a fluid-line section, which shows the V p /Vs anomaly (Figure
13).
CONCLUSION AND DISCUSSION
We implemented an AVO inversion algorithm in V (x, z) media. Our approach is a
two-step inversion scheme:
• 2.5-D Kirchhoff inversion;
• AVO coefficient estimation.
Since the velocity model used in AVO analysis is relatively smooth, the finite-difference
forward modeling result is accurate enough in Kirchhoff inversion. Both the synthetic
and field data example can verify the accuracy of the finite-difference scheme. On
the basis of Bleistein et al. (1987), we proposed another pair of Kirchhoff inversion
operators that have a more obvious physical meaning. One is the specular reflection
coefficient R, and the other is R multiplied by the cosine of half of the specular incident angle, R0 = R cos θ. The reflection coefficient R, organized into common-image
gathers, is not only necessary in estimating the AVO intercept A and slope B, but also
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Figure 10: Common-image gathers of the Mobil AVO dataset. (L) CIG from the
old velocity model. (R) CIG from the new velocity model. Both are from the same
midpoint location. The new model is significantly better than the old one. In the old
CIG, because of the use of a low velocity model, not only can the image event not be
flattened, it also has a depth shift from top to bottom. yalei1-mobil-cig [NR]
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Figure 11: The stacked section of the inversion result. (Top) Reflection coefficient R. (Bottom) R cos θ. The diffraction energy has been well collapsed.
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Figure 12: AVO coefficient sections of the Mobil AVO dataset. (Top) Intercept A
section. (Bottom) Slope B section. Similar to the stacked section, the diffraction
energy has also been well collapsed in the A and B section. Generally, intercept A
and slope B have opposite polarities. Slope B has a larger value than intercept A.
yalei1-mobil-avo [NR]
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Figure 13: The fluid-line section of the Mobil AVO dataset. Many strong events in
A & B section have been canceled. The strong event in the middle of the section
shows the anomaly of Vp /Vs , which may be an indicator of hydrocarbon in that area.
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essential to update the velocity model. Through checking common-image gathers, we
can update the velocity model and produce a more accurate image. This feature will
also prevent the velocity error from propagating into the final AVO coefficients. One
of the fundamental differences between Kirchhoff inversion and Kirchhoff depth migration is that Kirchhoff inversion has an extra weighting function varying along the
integral curves. We investigated the relationship between the weighting function and
double-square-root (DSR) equation in the homogeneous medium. It is interesting to
see that the weighting function has double peaks in the common-offset configuration.
This observation tells us that the largest contribution to the image is not from the
middle of the integral curve, but from the two flanks. Therefore, it is very important to include the locations of the two peaks in order to recover a true-amplitude
image. We applied our algorithm to both synthetic and field datasets. The synthetic
example shows that this new scheme is very accurate in calculating the reflection coefficient and the specular incident angle. When applying our approach to the Mobil
AVO dataset, we updated the velocity model according to the common-image gathers. Furthermore, we estimated the AVO coefficients, intercept A and slope B, and
then created a fluid-line expression of Vp/Vs anomaly. Our result shows that there is
a strong Vp /Vs anomaly in the middle section that suggests a potential hydrocarbon
indicator.
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